Why use JavaScript for machine learning?

Python has always been and remains the language of choice for machine learning, in part due to the maturity of the language, in part due to the maturity of the ecosystem, and in part due to the positive feedback loop of early ML efforts in Python. Recent developments in the JavaScript world, however, are making JavaScript more attractive to ML projects. I think we will see a major ML renaissance in JavaScript within a few years, especially as laptops and mobile devices become ever more powerful and JavaScript itself surges in popularity.

Advantages and challenges of JavaScript

JavaScript, like any other tool, has its advantages and disadvantages. Much of the historical criticism of JavaScript has focused on a few common themes: strange behavior in type coercion, the prototypical object-oriented model, difficulty organizing large codebases, and managing deeply nested asynchronous function calls with what many developers call callback hell. Fortunately, most of these historic gripes have been resolved by the introduction of ES6, that is, ECMAScript 2015, a recent update to the JavaScript syntax.

Con: Immature ecosystem for machine learning development

Despite the recent language improvements, most developers would still advise against using JavaScript for ML for one reason: the ecosystem. The Python ecosystem for ML is so mature and rich that it’s difficult to justify choosing any other ecosystem. But this logic is self-fulfilling and self-defeating; we need brave individuals to take the leap and work on real ML problems if we want JavaScript’s ecosystem to mature. Fortunately, JavaScript has been the most popular programming language on GitHub for a few years running, and is growing in popularity by almost every metric.

Pro #1: JavaScript is the most popular web development language with a mature npm ecosystem

There are some advantages to using JavaScript for ML. Its popularity is one; while ML in JavaScript is not very popular at the moment, the language itself is. As demand for ML applications rises, and as hardware becomes faster and cheaper, it’s only natural for ML to become more prevalent in the JavaScript world. There are tons of resources available for learning JavaScript in general, maintaining Node.js servers, and deploying JavaScript applications. The Node Package Manager (npm) ecosystem is also large and still growing, and while there aren’t many very mature ML packages available, there are a number of well built, useful tools out there that will come to maturity soon.

Pro #2: JavaScript is now a general purpose, cross-platform programming language

Another advantage to using JavaScript is the universality of the language. The modern web browser is essentially a portable application platform which allows you to run your code, basically without modification, on nearly any device. Tools like electron (while considered by many to be bloated) allow developers to quickly develop and deploy downloadable desktop applications to any operating system. Node.js lets you run your code in a server environment. React Native brings your JavaScript code to the native mobile application environment, and may eventually allow you to develop desktop applications as well. JavaScript is no longer confined to just dynamic web interactions, it’s now a general-purpose, cross-platform programming language.

Finally, using JavaScript makes ML accessible to web and frontend developers, a group that historically has been left out of the ML discussion. Server-side applications are typically preferred for ML tools, since the servers are where the computing power is. That fact has historically made it difficult for web developers to get into the ML game, but as hardware improves, even complex ML models can be run on the client, whether it’s the desktop or the mobile browser.

If web developers, frontend developers, and JavaScript developers all start learning about ML today, that same community will be in a position to improve the ML tools available to us all tomorrow. If we take these technologies and democratize them, expose as many people as possible to the concepts behind ML, we will ultimately elevate the community and seed the next generation of ML researchers.

Summary

In this article, we’ve discussed the important moments of JavaScript’s history as applied to ML. We’ve discussed some advantages to using JavaScript for machine learning, and also some of the challenges we’re facing, particularly in terms of the machine learning ecosystem.